Comparison of field and imaging spectroscopy to optimize soil organic carbon and nitrogen estimation in field laboratory conditions

• Modelling accuracy depends on the type of spectroradiometer and wavelength range. • Shortwave infrared (SWIR) outperformed the visible to near-infrared (VNIR) range. • Best models included bands over the whole spectrum. • Imaging spectrometer slightly better than field spectroradiometer in the VNIR range. Regenerative agriculture (RA) aims to improve soil health, water retention capacity, and resilience through sustainable regeneration and retention of soil organic carbon (SOC) and soil nitrogen (SN). Although laboratory analysis offers a reliable method for measuring SOC and SN, access to such facilities can be limited in remote areas and inconvenient, particularly if a large number of samples need to be analyzed. To address this, we compared two hyperspectral sensors, SVC HR-1024i field spectroradiometer (FS) and Specim IQ imaging spectrometer (IS), to estimate SOC and SN using 157 soil samples collected from agricultural sites in Taita Hills, Kenya. Reference SOC and SN content (%) were analyzed in the laboratory, and spectral measurements and images of the air-dried soil samples were generated using protocols suitable for field laboratories. Finally, we employed partial least squares regression (PLSR), Gaussian process regression (GPR), and least absolute shrinkage and selection operator (LASSO) to estimate SOC and SN from the preprocessed soil spectra over different wavelength ranges. Our best models yielded better accuracy for SOC estimation ( R 2 = 0.83 and RMSE = 0.36 %) and an R 2 of 0.70 with an RMSE of 0.07 % for SN using the field spectroradiometer. Both SOC and SN modeling over the full wavelength and shortwave-infrared (SWIR) regions achieved considerably better predictive accuracy than visible to near-infrared regions. These results suggest that FS with the SWIR region is best suited for SOC and SN estimation to support the planning and monitoring needs of RA initiatives.